Calculations show that injecting randomness into a quantum neural network could help it determine properties of quantum ...

Researchers have developed a hybrid CFD-neural network model for predicting TAIs in hydrogen-fueled turbines, improving ...

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...

Abstract: Broad learning system (BLS), as an innovative type of neural network, has demonstrated exceptional performance in regression tasks. Nonetheless, the majority of BLS methods, which rely on ...

This study proposes an important new approach to analyzing cell-count data, which are often undersampled and cannot be accurately assessed using traditional statistical methods. The case studies ...

The study, titled Reinforcement Learning for Monetary Policy Under Macroeconomic Uncertainty: Analyzing Tabular and Function ...

Although deep learning methods have made significant strides in single image sand dust removal, the heterogeneous uncertainty induced by dusty environments poses a considerable challenge. In response, ...

Neural networks aren’t the only game in artificial intelligence, but you’d be forgiven for thinking otherwise after the hot streak sparked by ChatGPT’s arrival in 2022. That model’s abilities, ...

Abstract: As seabed topography is closely related to the ocean gravity field, utilizing gravity data for seabed topography inversion has become the mainstream method. Although conventional deep neural ...

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...